Pancreas FACS Notebook
Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.
tissue_of_interest = "Pancreas"Load the requisite packages and some additional helper functions.
library(here)here() starts at /Users/olgabot/code/tabula-murislibrary(useful)Loading required package: ggplot2library(Seurat)Loading required package: cowplot
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsaveLoading required package: MatrixWarning: namespace 'Biobase' is not available and has been replaced
by .GlobalEnv when processing object 'call.'Warning: namespace 'lme4' is not available and has been replaced
by .GlobalEnv when processing object 'call.'Warning: namespace 'MatrixModels' is not available and has been replaced
by .GlobalEnv when processing object 'call.'Warning: namespace 'Biobase' is not available and has been replaced
by .GlobalEnv when processing object 'call.'Warning: namespace 'lme4' is not available and has been replaced
by .GlobalEnv when processing object 'call.'Warning: namespace 'MatrixModels' is not available and has been replaced
by .GlobalEnv when processing object 'call.'library(dplyr)Warning: package 'dplyr' was built under R version 3.4.2
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(Matrix)
save_dir = here('00_data_ingest', 'tissue_robj')# read the metadata to get the plates we want
plate_metadata_filename = here('00_data_ingest', 'facs_raw_data', 'metadata_FACS.csv')
plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadataSubset the metadata on the tissue.
tissue_plates = filter(plate_metadata, tissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_platesLoad the read count data.
#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', 'facs_raw_data', 'FACS', paste0(tissue_of_interest, '-counts.csv'))
raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)Make a vector of plate barcodes for each cell
plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
head(plate.barcodes)[[1]]
[1] "MAA000574"
[[2]]
[1] "MAA000574"
[[3]]
[1] "MAA000574"
[[4]]
[1] "MAA000574"
[[5]]
[1] "MAA000574"
[[6]]
[1] "MAA000574"MAA000574MAA000574MAA000574MAA000574MAA000574MAA000574Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to “expand” the per-plate metadata to be per-cell.
barcode.df = t.data.frame(as.data.frame(plate.barcodes))
rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df) plate.barcode
B21.MAA000574.3_8_M.1.1 "MAA000574"
D12.MAA000574.3_8_M.1.1 "MAA000574"
B22.MAA000574.3_8_M.1.1 "MAA000574"
D13.MAA000574.3_8_M.1.1 "MAA000574"
C1.MAA000574.3_8_M.1.1 "MAA000574"
D14.MAA000574.3_8_M.1.1 "MAA000574" MAA000574
MAA000574
MAA000574
MAA000574
MAA000574
MAA000574rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames
# Sort cells by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
corner(meta.data)raw.data = raw.data[, rownames(meta.data)]
corner(raw.data)Process the raw data and load it into the Seurat object.
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]
# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest,
min.cells = 5, min.genes = 5)
tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'
# Create metadata columns for annotations and subannotations
tiss@meta.data[,'annotation'] <- NA
tiss@meta.data[,'subannotation'] <- NACalculate percent ribosomal genes.
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")A sanity check: genes per cell vs reads per cell.
GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene", use.raw=T)
Filter out cells with few reads and few genes.
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"),
low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))Normalize the data, then regress out correlation with total reads
tiss <- NormalizeData(object = tiss)
tiss <- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))[1] "Regressing out nReads" "Regressing out percent.ribo"
[3] "Regressing out Rn45s"
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[1] "Scaling data matrix"
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|=================================================================| 100%tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)
Run Principal Component Analysis.
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = tiss)
Choose the number of principal components to use.
# Set number of principal components.
n.pcs = 11The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.
# Set resolution
res.used <- 0.5
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)[1] "1 singletons identified. 10 final clusters."To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)
Check expression of genes of interset.
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
How big are the clusters?
table(tiss@ident)
0 1 2 3 4 5 6 7 8 9
326 229 182 177 131 91 68 49 45 29 Which markers identify a specific cluster?
clust.markers0 <- FindMarkers(object = tiss, ident.1 = 0, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers1 <- FindMarkers(object = tiss, ident.1 = 1, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers2 <- FindMarkers(object = tiss, ident.1 = 2, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers3 <- FindMarkers(object = tiss, ident.1 = 3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers4 <- FindMarkers(object = tiss, ident.1 = 4, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers5 <- FindMarkers(object = tiss, ident.1 = 5, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers6 <- FindMarkers(object = tiss, ident.1 = 6, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers7 <- FindMarkers(object = tiss, ident.1 = 7, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers8 <- FindMarkers(object = tiss, ident.1 = 8, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers9 <- FindMarkers(object = tiss, ident.1 = 9, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)print(x = head(x= clust.markers0, n = 10)) p_val avg_diff pct.1 pct.2
Gcg 5.752502e-250 3.1916350 0.972 0.461
Ttr 1.161687e-236 2.7363907 0.991 0.710
Gpx3 2.058646e-214 1.6652366 0.997 0.479
Tmsb15b2 6.106572e-197 0.9782971 0.954 0.404
Cst3 1.129786e-182 0.8971441 1.000 0.953
Pappa2 7.263555e-174 0.9753057 0.966 0.168
Tmem27 1.552009e-171 1.4728245 0.991 0.558
Dbpht2 2.707538e-170 1.9946026 0.982 0.411
1700086L19Rik 2.971707e-167 1.5776327 0.985 0.457
Resp18 6.112076e-167 1.3200133 0.991 0.585You can also compute all markers for all clusters at once. This may take some time.
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)Display the top markers you computed above.
tiss.markers %>% group_by(cluster) %>% top_n(5, avg_diff)Assigning cell type identity to clusters
At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
0: alpha 1: beta 2: beta 3: exocrine 4: duct 5: delta 6: gamma 7: endothelial 8: immune 9: stellate
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
annotation <- c("pancreatic A cell", "type B pancreatic cell", "type B pancreatic cell", "pancreatic acinar cell", "pancreatic ductal cell", "pancreatic D cell", "pancreatic PP cell", "endothelial cell", "leukocyte", "pancreatic stellate cell")
cell_ontology_id <- c("CL:0000171", "CL:0000169", "CL:0000169", "CL:0002064", "CL:0002079", "CL:0000173", "CL:0002275", "CL:0000115", "CL:0000738", "CL:0002410")
tiss@meta.data[,'annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = annotation)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)
tiss@meta.data[tiss@cell.names,'annotation'] <- as.character(tiss@meta.data$annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 1.2, group.by='annotation')
JoyPlot(object = tiss, features.plot = c("Gcg", "Ppy"), group.by='annotation')Picking joint bandwidth of 0.186Picking joint bandwidth of 0.287
Checking for more markers for potential subclusters
Use more canonical markers to check for potential subclusters. For examples transcription factors are well defined for a number of cell types within pancreas. beta cell specific: Mafa, Nkx6-1, alpha cell specific: Mafb, Arx delta cell specific: Hhex islet specific: Isl1, Neurog3 (during development, unclear in adulthood) exocrine specific: Ptf1a duct specific: Hnf1b others: Pdx1 (pancreas-wide during embryogensis, beta and delta cell only in adulthood)
Checking for batch effects
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")
Print a table showing the count of cells in each identity category from each plate.
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
MAA000574 MAA000577 MAA000884 MAA000910 MAA001857 MAA001861 MAA001862
0 1 6 128 1 82 1 106
1 0 0 41 1 73 10 101
2 10 97 25 0 31 1 17
3 48 2 0 34 0 29 1
4 23 1 5 45 0 27 2
5 0 26 30 0 16 0 19
6 2 64 1 0 1 0 0
7 11 2 8 13 2 5 3
8 7 2 18 3 1 3 9
9 5 4 1 8 0 7 0
MAA001868
0 1
1 3
2 1
3 63
4 28
5 0
6 0
7 5
8 2
9 4Save the Robject for later
When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', 'tissue_seurat_robj',
paste0(tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)[1] "/Users/olgabot/code/tabula-muris/00_data_ingest/tissue_seurat_robj/Pancreas_seurat_tiss.Robj"save(tiss, file=filename)# To reload a saved object
# filename = here('00_data_ingest', 'tissue_seurat_robj',
# paste0(tissue_of_interest, "_seurat_tiss.Robj"))
# load(file=filename)Export the final metadata
So that Biohub can easily combine all your annotations, please export them as a simple csv.
head(tiss@meta.data)filename = here('00_data_ingest', 'tissue_annotation_csv',
paste0(tissue_of_interest, "_annotation.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','annotation','cell_ontology_id')], file=filename)---
title: "Pancreas FACS Notebook"
output: html_notebook
---

Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

```{r}
tissue_of_interest = "Pancreas"
```

Load the requisite packages and some additional helper functions.

```{r}
library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)

save_dir = here('00_data_ingest', 'tissue_robj')
```



```{r}
# read the metadata to get the plates we want
plate_metadata_filename = here('00_data_ingest', 'facs_raw_data', 'metadata_FACS.csv')

plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadata
```

Subset the metadata on the tissue.

```{r}
tissue_plates = filter(plate_metadata, tissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_plates
```

Load the read count data.
```{r}
#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', 'facs_raw_data', 'FACS', paste0(tissue_of_interest, '-counts.csv'))

raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)
```
Make a vector of plate barcodes for each cell

```{r}
plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
head(plate.barcodes)
```

Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to "expand" the per-plate metadata to be per-cell.
```{r}
barcode.df = t.data.frame(as.data.frame(plate.barcodes))

rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df)

rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames

# Sort cells by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
corner(meta.data)
raw.data = raw.data[, rownames(meta.data)]
corner(raw.data)
```

Process the raw data and load it into the Seurat object.

```{r}
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]

# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest, 
                    min.cells = 5, min.genes = 5)

tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'

# Create metadata columns for annotations and subannotations
tiss@meta.data[,'annotation'] <- NA
tiss@meta.data[,'subannotation'] <- NA
```


Calculate percent ribosomal genes.

```{r}
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")
```

A sanity check: genes per cell vs reads per cell.

```{r}
GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene", use.raw=T)
```

Filter out cells with few reads and few genes.

```{r}
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"), 
    low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))
```


Normalize the data, then regress out correlation with total reads
```{r}
tiss <- NormalizeData(object = tiss)
tiss <- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))
tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)
```


Run Principal Component Analysis.
```{r}
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
```

```{r, echo=FALSE, fig.height=4, fig.width=8}
PCHeatmap(object = tiss, pc.use = 1:3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 8)
```

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

```{r}
PCElbowPlot(object = tiss)
```

Choose the number of principal components to use.
```{r}
# Set number of principal components. 
n.pcs = 11
```


The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale...higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

```{r}
# Set resolution 
res.used <- 0.5

tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

To visualize 
```{r}
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)
```

```{r}
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)
```

Check expression of genes of interset.

```{r, echo=FALSE, fig.height=12, fig.width=8}
genes_to_check = c('Chga', 'Ins2', 'Gcg', 'Sst', 'Ppy', 'Ghrl', 'Krt19', 'Amy2b', 'Pecam1', 'Pdgfra', 'Pdgfrb', 'Ptprc')
FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 3, cols.use = c("grey", "blue"))
```

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

```{r, echo=FALSE, fig.height=4, fig.width=8}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(tiss, genes_to_check, plot.legend = T)
```

How big are the clusters?
```{r}
table(tiss@ident)
```

Which markers identify a specific cluster?

```{r}
clust.markers0 <- FindMarkers(object = tiss, ident.1 = 0, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers1 <- FindMarkers(object = tiss, ident.1 = 1, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers2 <- FindMarkers(object = tiss, ident.1 = 2, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers3 <- FindMarkers(object = tiss, ident.1 = 3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers4 <- FindMarkers(object = tiss, ident.1 = 4, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers5 <- FindMarkers(object = tiss, ident.1 = 5, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers6 <- FindMarkers(object = tiss, ident.1 = 6, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers7 <- FindMarkers(object = tiss, ident.1 = 7, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers8 <- FindMarkers(object = tiss, ident.1 = 8, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
clust.markers9 <- FindMarkers(object = tiss, ident.1 = 9, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

```{r}
print(x = head(x= clust.markers0, n = 10))
```

You can also compute all markers for all clusters at once. This may take some time.
```{r}
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

Display the top markers you computed above.
```{r}
tiss.markers %>% group_by(cluster) %>% top_n(5, avg_diff)

```


## Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:

0: alpha
1: beta
2: beta
3: exocrine
4: duct
5: delta
6: gamma
7: endothelial
8: immune
9: stellate

```{r}
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
annotation <- c("pancreatic A cell", "type B pancreatic cell", "type B pancreatic cell", "pancreatic acinar cell", "pancreatic ductal cell", "pancreatic D cell", "pancreatic PP cell", "endothelial cell", "leukocyte", "pancreatic stellate cell")
cell_ontology_id <- c("CL:0000171", "CL:0000169", "CL:0000169", "CL:0002064", "CL:0002079", "CL:0000173", "CL:0002275", "CL:0000115", "CL:0000738", "CL:0002410")

tiss@meta.data[,'annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = annotation)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'annotation'] <- as.character(tiss@meta.data$annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)

TSNEPlot(object = tiss, do.label = TRUE, pt.size = 1.2, group.by='annotation')
```

```{r}
JoyPlot(object = tiss, features.plot = c("Gcg", "Ppy"), group.by='annotation')
```



## Checking for more markers for potential subclusters
Use more canonical markers to check for potential subclusters. For examples transcription factors are well defined for a number of cell types within pancreas. 
beta cell specific: Mafa, Nkx6-1,
alpha cell specific: Mafb, Arx
delta cell specific: Hhex
islet specific: Isl1, Neurog3 (during development, unclear in adulthood)
exocrine specific: Ptf1a
duct specific: Hnf1b
others: Pdx1 (pancreas-wide during embryogensis, beta and delta cell only in adulthood)

```{r, echo=FALSE, fig.height=12, fig.width=8}
genes_to_check_TF = c('Ins2', 'Ins1', 'Pcsk1', 'Mafa', 'Nkx6-1', 'Hk1', 'Ldha', 'Mafb', 'Arx', 'Gcg', 'Hhex', 'Pdx1', 'Sst','Isl1', 'Neurog3', 'Chga', 'Ptf1a', 'Hnf1b', 'Cpa1')
FeaturePlot(tiss, genes_to_check_TF, pt.size = 1, nCol = 3, cols.use = c("grey", "blue"))
```


## Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.
```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")
```

Print a table showing the count of cells in each identity category from each plate.

```{r}
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
```

# Save the Robject for later
When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', 'tissue_seurat_robj', 
                     paste0(tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```

```{r}
# To reload a saved object
# filename = here('00_data_ingest', 'tissue_seurat_robj', 
#                      paste0(tissue_of_interest, "_seurat_tiss.Robj"))
# load(file=filename)
```



# Export the final metadata

So that Biohub can easily combine all your annotations, please export them as a simple csv.

```{r}
head(tiss@meta.data)
```


```{r}
filename = here('00_data_ingest', 'tissue_annotation_csv', 
                     paste0(tissue_of_interest, "_annotation.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','annotation','cell_ontology_id')], file=filename)
```